How Daily and Seasonal RhythmsImpact Cows

Kevin Harvatine, Ph.D.Associate Professor of Nutritional Physiology

Penn State UniversityKJH182@psu.edu

How Daily and Seasonal Rhythms Impact Cows

Presented during the 2020 Four State Dairy Nutrition & Management Virtual Conference. Do not reuse or reproduce without author permission.

Presenter’s Name: Kevin Harvatine, Ph.D.Associate Professor of Nutritional PhysiologyPenn State University KJH182@psu.edu

Decreased by milk fatdepressionUnsaturated fatFermentabilityAcidosisFeeding strategiesIonophores

Increase by additional substrateAcetate from foragesFat supplement

Palmitic acid

Nutritional Factors Non nutritional Factors

Milk fat

Milk fat and protein are affected by manyfactors

Genetics

Season

Time of dayStage of lactation

Parity

Milk protein also impacted by diet andother similar non nutritional factors

Seasonal rhythms coordinate physiology (metabolism) with the environment:

Amazing examples in nature!

Seasonal Breeding in Sheep

Day

Night

NightDay

Migration

Hibernation

Daily rhythms coordinate metabolism with changes across the day

Most processes in the body follow a 24 h cycle- Activity and Alertness- Nutrient Metabolism- Milk Synthesis- Intake

Why??Allows the animal to anticipate changes and adapt before they occur

Key Principles• There is a seasonal pattern of milk composition

and yield driven by day length and change in day length

• There is a daily (circadian) pattern of intake that has a major impact on the rumen and there is a daily pattern of milk synthesis

• Considering seasonal and daily patterns provide additional avenues to optimize milk production and profitability

• Main environmental cues:– Light/Dark– Feeding Times– Milking Time?

• A breakdown in the systemcreates jetlag!

• A disconnection betweenlighting and timing cancause metabolic issues inhumans and rodents

Example is night shift work inhumans

Asher, Schibler 2011

How does the cow know what time of year and day it is?

PeripheralClocks

Environmental CuesLight/Dark

OtherEnvironmental

Cuese.g. FeedingTimes

Master Clock(SCN Brain)

1 2

3 4

5 6

We know “Photoperiod” has a large impact on milk yield Constant 16 to 18 h vs. 8

to 10 h light

• ~5 to 10% increase in milk yield and no change in milk composition

• Additional effect of short days in dry period

• Eliminated by constant light

-Basic mechanism of photoperiod is through same signaling as circadian rhythms

Dahl and Petitclerc., 2003

Short photoperiod during dry period increases milk yield in the next lactation!

Auchtung et al., 2005

- Spring calving cows would normally be dry during short days

- Likely driven by increased mammary development so more milk secreting cells

• Patterns that repeat every year

• Mostly driven by- day length - lengthening/shortening

days- change in day length

• Regulated through the same molecular system as circadian rhythms

Some Amazing Examples in Biology

Seasonal rhythms are common in many animals

2.75

2.95

3.15

3.35

3.55

3.75

3.95

4.15

1/20

0010/2000

7/20

014/20

021/20

0310/2003

7/20

044/20

051/20

0610/2006

7/20

074/20

081/20

0910/2009

7/20

104/20

111/20

1210/2012

7/20

134/20

141/20

1510/2015

7/20

164/20

171/20

1810/2018

Percento

fMilk

Date

Upper Midwest

Seasonal Pattern of Milk Fat & Protein: Upper Midwest US Milk Market

Fat

Protein

~0.25Units

~0.20Units

The annual rhythms occurs in all US milk Markets. Percent fat has a larger amplitude in north and smaller in south

- All milk markets fit a cosine function with a very good fit

Salfer et al. 2019

State Range, lb Acrophase

MN 5.3a Apr 22PA 5.3b Apr 15TX 7.9c April 7FL 9.2d April 9

There is also an annual rhythm to milk yield: Data from PA, MN, FL, and TX

Salfer et al. 2020

2022242628303234

Milk

yie

ld, k

g

0

Florida (FL)Minnesota (MN)Pennsylvania (PA)Texas (TX)

Month

7 8

9

11 12

10

800850900950

100010501100115012001250

Milk

fat y

ield

, g

0

State Range, lb PeakPA 0.26 Feb 23MN 0.20 Feb 27TX 0.31 March 13FL 0.29 March 31

Milk fat percent peaks at end of year, but milk fat yield peaks in March and differ by region

Salfer et al. 2020

FL

TX

PA & MN

State Range, % PeakPA 0.28 Jan 4MN 0.34 Jan 5TX 0.28 Jan 3FL 0.24 Jan 2

3.00

3.20

3.40

3.60

3.80

4.00

Milk

fat c

onc.

, %

0

Florida (FL) Minnesota (MN)

Pennsylvania (PA) Texas (TX)

Month Month

Most of the seasonal variation in milk fat is due to de novo synthesis <16 C FA (40 herds)

40 St. Albans Coop herds

Dann 2019 PSU Dairy Nutr. Workshop

Milk Fat, %

De novo FA, % Milk

There is an annual pattern to milk protein!

State Range, % PeakPA 0.18 Dec. 21MN 0.20 Dec. 22TX 0.22 Dec. 17FL 0.12 Dec. 1

State PeakMN Feb 24a

PA Mar 2b

TX Mar 6c

FL Mar 19d

700

750

800

850

900

950

1000

Milk

pro

tein

yie

ld, g

Month

02.75

2.85

2.95

3.05

3.15

3.25

Milk

pro

tein

con

c., %

Month

0

Florida (FL) Minnesota (MN)

Pennsylvania (PA) Texas (TX)

The seasonal pattern is consistent by parity

Fat, %

Protein, %

Milk Yield

What does heat stress do to milk yield and composition?

• Generally a decrease in milk yield and milk protein percent and an increase in fat percent

Reference MY, kg Fat, % Prot, %Rungruang et al. 2014 -3.4 0.20 -0.10Baumgard et al. 2011 -6.2 0.28 -0.12Zimbelman et al. 2010 -0.1 -0.17 0.13Wheelock et al. 2010 -9.6 0.60 -0.27Rhoads et al. 2009 -10.6 0.34 -0.13

Schwartz et al. 2009 -10.1 0.06 -0.22

Two seasonal time-keepers:• Milk composition is driven by lengthening and

shortening days and aligns with the solstice• Milk yield is driven by rate of change in day

length and aligns with the equinox

Constant long days appears to be setting physiology of the spring equinox (increased milk yield and no change in composition)

- No data on how to manage out of this. Managing photoperiod probably best chance

What do I think is going on?

13 14

15 16

17 18

PeakLact.

LateLact.

Sheanhan, Kolver, and Roche, 2011

Is there a daily pattern of feed intake?Pasture Fed Cows

DeVries et al. 2005

TMR fed cows: Feeding time is most importantFeeding and milking commonly both near dawn & dusk

Eating

Ruminating

Sheanhan, Kolver, and Roche, 2011

Eating and Ruminating tend to be inverse Rumination pattern is maintained even during heat stress

Soriani et al. JDS 2014

Daily pattern of rumination time expressed in minutes per 2 h in 3 levels of daily maximum temperature-humidity index (THI). White bars = THI <80; bars with vertical lines = THI from 80 to 85; black bars = THI >85.

Low, Medium, and high Temperature-Humidity Index

PSU Feeding Behavior System

MooMonitor+ Dairymaster(Image Dairymaster.ie)

Rate of feed intake is variable over the day

Ying et al. 2015

0.0

0.5

1.0

1.5

2.0

2.5

0030 0430 0830 1230 1630 2030

DM

I, kg

/h

l

Time of Day

0.00.10.20.30.40.50.60.70.80.91.0

0030 0430 0830 1230 1630 2030

Star

ch In

take

, kg/

h

Time of Day

19 20

21 22

23 24

What is the impact of the daily pattern of intake?

Intake = Entrance of fermentable feed into the rumen

for microbes to digest

Fermentable feed = Synthesis of VFA s (acids) & microbial

protein

VFA s = Acid load for rumen

Nutrient supply for cow

What is in the rumen changes relative to feeding

Ying et al. 2015

10111213141516

0000 0600 1200 1800

Rum

en D

M P

ool,

kg

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0000 0600 1200 1800Rum

en S

tsrc

h Po

ol, k

g

Time of Day

5.65.75.85.96.06.16.26.36.4

0030 0430 0830 1230 1630 2030

Rum

en p

H

Time of Day

How flexible is the daily pattern of feedintake?

• Feeding stimulates intake, but what is the impact offeeding time

• Fed TMR:• 1x/d at 0830 h (AM)

• 1x/d at 2030h (PM)

• 2x/d at 0830 and 2030 h (AMPM)

AM vs PM feeding had no effect of DMI ormilk production

No treatment effects on daily milk production andcomposition or DMI.

P valueTreatment Means Contrasts

Item AM PM AMPM SE Trt AM vs.PM

AM vs.AMPM

Yield, lbs/dMilk 110.0 111.1 111.8 5.7 0.69 0.59 0.40Milk fat 3.78 3.78 3.85 0.09 0.84 0.99 0.62Milk protein 3.26 3.28 3.30 0.13 0.77 0.78 0.48

Milk composition, %Fat 3.51 3.49 3.48 0.15 0.90 0.83 0.66Protein 2.97 2.95 2.96 0.07 0.80 0.52 0.69DMI, lbs/d 71.7 69.1 70.2 2.0 0.40 0.18 0.44Feed Efficiency 1.54 1.58 1.57 0.05 0.43 0.21 0.37

• Also no difference in milk FA profile

Evening feed delivery increased feed intake afterfeeding by >50%!

Effect P valueTreatment 0.78Time <0.01Treatment x Time <0.01

• Conditional meals were larger at the evening feeding

• Modestly higher intake rate in the early afternoon for AM

Treatment Phase/h Amplitude P valueAM 1654 2.0 <0.01PM 1638* 0.6* <0.01AMPM 1448* 1.1* <0.01

Circadian Parameters

*Significantly (P < 0.05) different from AM

AM vs.PM (O = P < 0.01, and O = P < 0.05); AM vs. AMPM (T P < 0.01, and T P < 0.05)

ANOVA

Increase intake in the evening spikes insulin

Effect P valueTreatment 0.76Time <0.01Treatment x Time <0.01

Circadian Parameters

*Significantly (P < 0.05) different from AM

• Fresh feed delivery at night resulted in greater insulin secretion

• Morning feeding moderately increased insulin in the early afternoon

ANOVA

AM vs.PM (O = P < 0.01, and O = P < 0.05); AM vs. AMPM (T P < 0.01, and T P < 0.05)

Treatment Phase/h Amplitude P valueAM 1844 1.8 0.07PM 0031* 8.3* <0.01AMPM 2220* 4.8* <0.01

25 26

27 28

29 30

Milk Milk

Quist et al. 2008

Milk synthesis is variable over the day2x Milked Herds

2 kg0.5 Units

Milk

Quist et al. 2008

Milk yield is variable over the day3x Milked Herds

0.3 Units 1.8 kg

Theoretical de-synchronization of intake and mammary metabolism

First test: Fed cows 1x/d or 4x/d in equal feedings

Feeding cows 1x/d vs 4x/d changed milk yield over the day at one milking

Effect PTrt 0.64Time <0.001Trt*Time 0.05

**

** P< 0.01

Trt MY, kg/d1x Fed 47.34x Fed 47.1SEM 0.64

Rottman et al. 2014

1x4x 4x 4x 4x

10.510.710.911.111.311.511.711.912.112.312.5

-100 500 1100 1700 2300

Milk

Yie

ld, k

g/6

h

Time, h

1x Fed

4x Fed

Feeding 4x/d increased milk fat and decreased amplitude over the day

Effect PTrt <0.001Time <0.001Trt*Time <0.05

2.00

2.50

3.00

3.50

4.00

-0100 0500 1100 1700 2300

Fat P

erce

nt, %

Time, h

1x Fed 4x Fed

1x4x 4x 4x 4x

***

****

TrtDaily AVGFat %

1x Fed 3.094x Fed 3.35SEM 0.15

* P< 0.05; ** P< 0.01; *** P< 0.001

Rottman et al. 2014

Effect Pr > FTrt <0.001Time <0.01Trt*Time 0.05

300

325

350

375

400

425

-0100 0500 1100 1700 2300

Fat Y

ield

, g/6

h

Time, h

1x Fed4x Fed**

†

** P< 0.01; † P< 0.1

Trt Fat, g/d1x Fed 14654x Fed 1592SEM 90

1x4x 4x 4x 4x

Rhythm of milk fat yield also modified by 4x feeding

Rottman et al. 2014

31 32

33 34

35 36

Hogeveen et al., 2001

When do cows prefer to be milked??Automated Milking System How Can We Use This Information??

Think not just about the diet we arefeeding, but how we are feeding itand how the cows are eating it!

We need to watch the cows and seewhat they are doing!

1st… Think of the rumen

• Can we stabilize the amount of fermentablefeed entering the rumen over the day?

– Take out some of the slugs and fill in during someof the low points

How do we do this?

• Feed delivery is a strong signal for feedingwhich can be used to increase intake duringlow intake periods of the day

• Make sure feed is available when return fromparlor………, but– Delivery of feed 2 3 h before or after milking mayspread intake more across the day??

What else can we do?

• Feeding different diets across the day mightalso work– Feed same ration to entire herd in morning

– Return to “top off” high groups

Interesting Call From the Field• One pen of cows on a large farm consistently0.3 to 0.5 units lower in milk fat than peer penin another barn fed same diet

• Moved fifteen cows from the pen to anotherpen and they increased milk fat

• Normal MFD troubleshooting turned up noclues

• Cows being fed later in the day (11:30 AM)• Switched milking and feeding order so feeddelivered earlier and before milking.

• Milk fat increased equal to peer pen

37 38

39 40

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Must Consider Multiple Factors That Have anImpact on Behavior

Key Principles

• There is a daily (circadian) pattern of intake that has a major impact on the rumen

• There is a daily pattern of milk synthesis

• We need to manage the daily pattern of intake and our best tools for this are through feeding and milking schedules

• Don’t be afraid to feed multiple diets per day, but be careful with late afternoon and evening feedings (early morning may be safer)

Thank You

Lab Members:Cesar Matamoros, Beckie Bomberger, Alanna Staffin, Reilly Pierce, Ahmed Elzennary, and Rachel Walker.

Previous Lab Members:Chengmin Li, Elle Andreen, Dr. Isaac Salfer, Dr. Daniel Rico, Dr. Michel Baldin, L. Whitney Rottman, Mutian Niu, Dr. Natalie Urrutia, Richie Shepardson, Andrew Clark, Dr. Liying Ma, Elaine Brown, and Jackie YingDisclosuresK.J. Harvatine’s research in the past 10 years were partially supported by the Agriculture and Food Research Initiative Competitive Grant No. 2010-65206-20723, 2015-67015-23358, 2016-68008-25025, and 2018-06991 from the USDA National Institute of Food and Agriculture [PI Harvatine], USDA Special Grant 2009-34281-20116 [PI Harvatine], Berg-Schmidt, Elanco Animal Health, BASF, Novus International, PA Soybean Board, Phode Laboratories, Kemin International, Milk Specialties Global, Adisseo, Micronutrients Inc., Organix Recylcing, Insta-Pro Intl., and Penn State University. Harvatine has consulted for Milk Specialties Global, a manufacturer of prilled saturated fat supplements and Micronutrients Inc. as a member of their science advisory boards. Harvatine has also received speaking honorariums from Elanco Animal Health, Novus International, Cargill, Virtus Nutrition, Chr Hansen, NDS, Nutreco, Mycogen, and Milk Specialties Global in the past three years.

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